Service providers and device manufacturers (e.g., wireless, cellular, etc.) are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services. One area of development has been the use of technology for automated recognition of faces, people, and other objects or features (e.g., recognition of expressions such as facial expressions, body gestures, movement, voice, sound, etc.) within media content such as images, video streams, and audio streams. For example, many modern communication devices (e.g., smartphones, handsets, etc.) are commonly equipped with cameras and other sensors (e.g., microphones) that enable the devices to perform such recognition (e.g., facial, voice, expression recognition, etc.) on captured content. However, these devices often employ conventional methods for facial and/or object recognition that have traditionally struggled to perform accurately under certain conditions (e.g., noise, varying expressions, bad angle, poor lighting, low resolution images or sounds, etc.). Accordingly, service providers and device manufactures face significant technical challenges to improving the accuracy of facial and/or object recognition.